Steerable optical flow based image registration: application to aligning human torso images
Bibliographic record
Abstract
The aim of image registration is to align two or more images taken from different viewpoints, at different time instances, or by different modalities. Image registration methods are divided into two main categories, feature based and intensity based methods. Recently intensity based methods have gained popularity since they aim at finding a dense correspondence between the images needed to be aligned without calculating correspondence between salient features. In this work, a new intensity based image registration method has been proposed and tested. This method models the source and target image as a single image displaced over time and calculates the optical flow fields in a multiresolution framework. In order to have the ability to represent complex fields, the deformation has been modelled as locally affine but globally smooth. Multiresolution image representation by steerable pyramid decomposition is integrated with the differential image registration technique in order to find accurate image deformations. The usage of steerable pyramid overcomes traditional problems in other pyramidal methods namely aliasing across different bands, lack of translation and rotation invariance. The new algorithm was validated using torso images for volunteers at the University of Alberta in addition to images captured of a cast model of the human torso. Experiments have demonstrated promising results in terms of root mean square error and average pixel error.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".